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      Heavy Metal Tolerance in Plants: Role of Transcriptomics, Proteomics, Metabolomics, and Ionomics


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          Heavy metal contamination of soil and water causing toxicity/stress has become one important constraint to crop productivity and quality. This situation has further worsened by the increasing population growth and inherent food demand. It has been reported in several studies that counterbalancing toxicity due to heavy metal requires complex mechanisms at molecular, biochemical, physiological, cellular, tissue, and whole plant level, which might manifest in terms of improved crop productivity. Recent advances in various disciplines of biological sciences such as metabolomics, transcriptomics, proteomics, etc., have assisted in the characterization of metabolites, transcription factors, and stress-inducible proteins involved in heavy metal tolerance, which in turn can be utilized for generating heavy metal-tolerant crops. This review summarizes various tolerance strategies of plants under heavy metal toxicity covering the role of metabolites (metabolomics), trace elements (ionomics), transcription factors (transcriptomics), various stress-inducible proteins (proteomics) as well as the role of plant hormones. We also provide a glance of some strategies adopted by metal-accumulating plants, also known as “metallophytes.”

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          Genome-wide analysis of the ERF gene family in Arabidopsis and rice.

          Genes in the ERF family encode transcriptional regulators with a variety of functions involved in the developmental and physiological processes in plants. In this study, a comprehensive computational analysis identified 122 and 139 ERF family genes in Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa L. subsp. japonica), respectively. A complete overview of this gene family in Arabidopsis is presented, including the gene structures, phylogeny, chromosome locations, and conserved motifs. In addition, a comparative analysis between these genes in Arabidopsis and rice was performed. As a result of these analyses, the ERF families in Arabidopsis and rice were divided into 12 and 15 groups, respectively, and several of these groups were further divided into subgroups. Based on the observation that 11 of these groups were present in both Arabidopsis and rice, it was concluded that the major functional diversification within the ERF family predated the monocot/dicot divergence. In contrast, some groups/subgroups are species specific. We discuss the relationship between the structure and function of the ERF family proteins based on these results and published information. It was further concluded that the expansion of the ERF family in plants might have been due to chromosomal/segmental duplication and tandem duplication, as well as more ancient transposition and homing. These results will be useful for future functional analyses of the ERF family genes.
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              Correlation between protein and mRNA abundance in yeast.

              We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243-251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.

                Author and article information

                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                08 February 2016
                : 6
                : 1143
                [1] 1Ranjan Plant Physiology and Biochemistry Laboratory, Department of Botany, University of Allahabad Allahabad, India
                [2] 2Department of Botany, Government Ramanuj Pratap Singhdev Post Graduate College, Sarguja University Baikunthpur, India
                Author notes

                Edited by: Zuhua He, Shanghai Institute for Biological Sciences, China

                Reviewed by: Carla Antonio, Instituto de Tecnologia Química e Biológica, Portugal; Dai-Yin Chao, Shanghai Institute of Plant Physiology and Ecology, China

                *Correspondence: Vijay P. Singh vijaypratap.au@ 123456gmail.com ;

                This article was submitted to Plant Physiology, a section of the journal Frontiers in Plant Science

                Copyright © 2016 Singh, Parihar, Singh, Singh and Prasad.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 14 August 2015
                : 02 December 2015
                Page count
                Figures: 4, Tables: 4, Equations: 0, References: 488, Pages: 36, Words: 33983
                Plant Science

                Plant science & Botany
                crop,heavy metal,ionomics,metabolomics,metallophytes,proteomics,transcriptomics,yield
                Plant science & Botany
                crop, heavy metal, ionomics, metabolomics, metallophytes, proteomics, transcriptomics, yield


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